An improved approach to determine aerosol properties from all-sky camera imagery: Sensitivity to the partially cloud scenes

• Published in: Atmospheric environment (1994) Vol. 327; p. 120495
• Main Authors: Scarlatti, F., Gómez-Amo, J.L., Valdelomar, P.C., Estellés, V., Utrillas, M.P.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-06-2024
• Subjects: Aerosol properties; All-sky camera; AOD and AE; Machine Learning; Partially cloud scenarios; Partially cloud scenarios; Aerosol properties; AOD and AE; All-sky camera; Machine Learning
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2024.120495

We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.
•Retrieval of aerosol properties AOD and AE in presence of partially cloudy sky.•Calibrated all-sky camera RGB signals related to Cimel direct aerosol properties with machine learning methodology.•Statistical evaluation of prediction performances of machine learning method.•Aerosol retrieval in partially cloud retrieval can help indirect effect studies.